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        <header><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/severaltransformationsofsignalprocessing/" target="_blank">信号处理的几种变换</a>
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                            <a href="/categories/%E4%BF%A1%E5%8F%B7%E5%A4%84%E7%90%86/"><span class="svg-icon icon-folder"></span>信号处理</a>
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        <div class="article-text"><div class="content summary-content">信号处理中通常会进行转换，使信号便于处理，提取信息，最基本的变换是傅里叶变换，后又衍生处了小波波变换，希尔伯特变换，希尔伯特黄变换，曲波变换</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/%E7%AE%97%E6%B3%95/" style="">算法</a></div>
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        <header><div class="featured-image-preview"><a href="/batchnormalization/" target="_blank"><img loading="lazy" decoding="async"
         
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         alt="经典算法：Batch Normalization"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/batchnormalization/" target="_blank">经典算法：Batch Normalization</a>
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                            <a href="/categories/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/"><span class="svg-icon icon-folder"></span>深度学习</a>
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        <div class="article-text"><div class="content summary-content">在卷积网络六大模块中的BN（批批标准化）所指的就是Batch Normalization，该算法15年提出，现在已经成为深度学习中经常使用的技</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/batch-normalization/" style="">Batch Normalization</a><a class="post-tag-summary" href="/tags/%E7%AE%97%E6%B3%95/" style="">算法</a></div>
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        <header><div class="featured-image-preview"><a href="/panorama/" target="_blank"><img loading="lazy" decoding="async"
         
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         alt="Python实现图像全景拼接"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/panorama/" target="_blank">Python实现图像全景拼接</a>
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                            <a href="/categories/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/"><span class="svg-icon icon-folder"></span>计算机视觉</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-08-09">2020-08-09</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">目标：将数张有重叠部分的图像通过特征点检测，匹配，图像变换拼成一幅无缝的全景图或高分辨率图像 在图像拼接中首先利用SIFT算法提取图像特征进而</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/%E5%AE%9E%E6%88%98/" style="">实战</a><a class="post-tag-summary" href="/tags/opencv/" style="">OpenCV</a><a class="post-tag-summary" href="/tags/sift/" style="">SIFT</a><a class="post-tag-summary" href="/tags/ransac/" style="">RANSAC</a></div>
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        <header><div class="featured-image-preview"><a href="/segmentstionclustering/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/20200731151831291.png"
         alt="图像分割(Segmentation)——K-Means, 最小割, 归一化图割"
         title="图像分割(Segmentation)——K-Means, 最小割, 归一化图割" height="798" width="238"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/segmentstionclustering/" target="_blank">图像分割(Segmentation)——K-Means, 最小割, 归一化图割</a>
                </h2><div class="post-meta summary-post-meta"><span class="meta-item post-category">
                            <a href="/categories/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/"><span class="svg-icon icon-folder"></span>计算机视觉</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-07-31">2020-07-31</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">图像分割是将图片将相似的部分分割成相同的块 Gestalt理论解释物体分割的底层原理 将同一个东西群组在一起,集合中的元素可以具有由关系产生的属</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/ransac/" style="">RANSAC</a><a class="post-tag-summary" href="/tags/%E6%8B%9F%E5%90%88/" style="">拟合</a><a class="post-tag-summary" href="/tags/%E9%9C%8D%E5%A4%AB%E5%8F%98%E6%8D%A2/" style="">霍夫变换</a></div>
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        <header><div class="featured-image-preview"><a href="/cannyhough/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/20200729181503649.png"
         alt="python实现Canny与Hough算法"
         title="python实现Canny与Hough算法" height="798" width="238"
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                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/cannyhough/" target="_blank">python实现Canny与Hough算法</a>
                </h2><div class="post-meta summary-post-meta"><span class="meta-item post-category">
                            <a href="/categories/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/"><span class="svg-icon icon-folder"></span>计算机视觉</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-07-29">2020-07-29</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">任务说明：编写一个钱币定位系统，其不仅能够检测出输入图像中各个钱币的边缘，同时，还能给出各个钱币的圆心坐标与半径。 效果 代码实现Canny边缘</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/canny/" style="">Canny</a><a class="post-tag-summary" href="/tags/hough/" style="">Hough</a><a class="post-tag-summary" href="/tags/%E7%AE%97%E6%B3%95/" style="">算法</a></div>
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        <header><div class="featured-image-preview"><a href="/texture/" target="_blank"><img loading="lazy" decoding="async"
         
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         alt="纹理表示(Texture)"
         title="纹理表示(Texture)" height="798" width="238"
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                    <a href="/texture/" target="_blank">纹理表示(Texture)</a>
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                            <a href="/categories/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/"><span class="svg-icon icon-folder"></span>计算机视觉</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-07-26">2020-07-26</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">纹理是由一些基元以某种方式组合起来，虽然看起来很“乱”，但任然存在一些规律 规则的纹理与不规则的纹理 纹理描述 使用高斯偏导核，对图像进行卷积，x</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/texture/" style="">Texture</a><a class="post-tag-summary" href="/tags/k%E5%9D%87%E5%80%BC/" style="">K均值</a></div>
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        <header><div class="featured-image-preview"><a href="/edgedetection2/" target="_blank"><img loading="lazy" decoding="async"
         
         src="https://img-blog.csdnimg.cn/20200721151228568.png"
         alt="区域检测——Blob &amp; SIFT"
         title="区域检测——Blob &amp; SIFT" height="798" width="238"
    /></a>
                </div><div style="padding-top: 20px;padding-left: 20px;padding-right: 20px;"><h2 class="summary-title" itemprop="name headline">
                    <a href="/edgedetection2/" target="_blank">区域检测——Blob &amp; SIFT</a>
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                            <a href="/categories/%E8%AE%A1%E7%AE%97%E6%9C%BA%E8%A7%86%E8%A7%89/"><span class="svg-icon icon-folder"></span>计算机视觉</a>
                    </span><span class="meta-item post-author"><a href="https://aimoon.top" title="Author" target="_blank" rel="noopener noreffer author" class="author"><span class="svg-icon icon-user"></span>Yasin</a></span><span class="meta-item post-publish"><span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-07-22">2020-07-22</time></span></div></div>
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        <div class="article-text"><div class="content summary-content">针对Harris无法拟合尺度问题而提出 目标:独立检测同一图像缩放版本的对应区域 需要通过尺度选择机制来寻找与图像变换协变的特征区域大小 “当尺度</div></div>
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            <div class="post-tags-summary-under-footer-display" style="display: inline-block;"><a class="post-tag-summary" href="/tags/laplacian/" style="">Laplacian</a><a class="post-tag-summary" href="/tags/blob/" style="">Blob</a><a class="post-tag-summary" href="/tags/sift/" style="">SIFT</a><a class="post-tag-summary" href="/tags/%E5%8C%BA%E5%9F%9F%E6%A3%80%E6%B5%8B/" style="">区域检测</a></div>
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         alt="区域检测——Harris角点"
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        <div class="article-text"><div class="content summary-content">对于图像处理时经常需要提取特征点分析图片结构，将照片进行拼接，实现全景拍摄，那么在照片特征点提取时所采用的具体算法是什么呢？ 解决思路 提取特征</div></div>
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        <div class="article-text"><div class="content summary-content">提取完边缘后如何使用数学模型来描述边缘？ 例如：在桌子上有几枚硬币，在经过边缘提取后，需要描述出硬币的圆心坐标和圆的大小 难点 噪声：噪声的存在使</div></div>
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        <div class="article-text"><div class="content summary-content">边缘提取在大多数时候图像的边缘可以承载大部分的信息，并且提取边缘可以除去很多干扰信息，提高处理数据的效率 目标识别图像中的突然变化(不连续) 图</div></div>
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        <div class="article-text"><div class="content summary-content">PAPER: Cars Can’t Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks CityScape数据集 介绍 ​Cityscapes是关于城市街道场景的语义理解图片数据集。它主要包含来</div></div>
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        <div class="article-text"><div class="content summary-content">Embedding独热码：数量大，过于稀疏，映射之间是独立的，没有表现出关联性 Embedding：一种单词编码方法，以低维向量实现了编码，这</div></div>
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        <div class="article-text"><div class="content summary-content">卷积就是特征提取器，通过卷积计算层提取空间信息，例如我们可以用卷积和提取一张图片的空间特征，再把提取到的空间特征送入全连接网络，实现离散数据</div></div>
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        <div class="article-text"><div class="content summary-content">InceptionNetInceptionNet诞生于2014年，当年ImageNet竞赛冠军，Top5错误率为6.67% Inception</div></div>
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